Abstract 6397: Understanding the glioblastoma microenvironment with spatial resolution in PDX models
Notice bibliographique
Résumé
Abstract Glioblastoma multiforme (GBM) is the most common adult brain tumour, and despite aggressive treatment, it recurs fatally. GBM tumours include diverse populations of malignant and non-neoplastic cells with distinct molecular capabilities and with differential levels of sensitivity to treatment. Understanding the cell dynamics that occur during the development of GBM resistance to therapy could reveal key aspects of this process, including how resistance is acquired in time and how the diverse cell types of the tumour microenvironment (TME) contribute to this phenotype. In addition to the role of the TME, GBM exhibits significant tumour heterogeneity with diverse genetic clones coexisting in the same tumor, as well as cells with similar genetic backgrounds capable of adopting distinct transcriptional states and subtypes. The complex and dynamic interactions between tumor and TME remain to be fully studied. This work focuses on the in vivo spatial organization in GBM during disease progression. We generated spatial transcriptomic data from a set of adult GBM samples grown as patient-derived xenograft (PDX) models, profiled at different time points of the disease. Three PDX lines from one GBM patient (derived from tumor core, vascularized area, and infiltrating front) were used to recapitulate the genetic and phenotypic heterogeneity observed in the human disease. Two replicates from each of 8 PDX mice were collected from early, mid, and late time points of tumour growth and data was generated using the 10X Genomics Visium platform. We developed a robust computational pipeline capable of distinguishing admixture of human (tumour) and mouse cells (TME), using state-of-the-art tools. Human and mouse cell types and states were identified using pooled and separate single-cell references of human GBM states, and mouse brain cells from both normal and tumour conditions. With this approach we observe spatially distinct patterns of both (a) tumour infiltration patterns specific to the each PDX line that includes non-random distribution of GBM transcriptional states and genetic clones, and (b) spatially distinct infiltration of TME components including microglial and macrophage populations. Overall, our approach addresses the challenge of understanding the tumor-TME relationship by application of spatial profiling in PDX models, and provides a computational pipeline complex multi-species analysis in the spatial transcriptomic field. Citation Format: Aly O. Abdelkareem, Katalin Osz, Donna Senger, Jennifer A. Chan, Sorana Morrissy. Understanding the glioblastoma microenvironment with spatial resolution in PDX models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6397.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».